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arXiv:2606.02198v1 Announce Type: new Abstract: Prediction tasks over individual futures, which are inherently noisy, often admit multiple similarly accurate models. When these models produce differen

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Model Releases

Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation

DGX agent

arXiv:2602.07083v2 Announce Type: replace-cross Abstract: Structural modeling is a fundamental component of computational engineering science, in which even minor physical inconsistencies or specifica

model-releasesarxiv-cs-ai

2 Jun 2026

RPCASSM: Robust PCA State Space Model For Infrared Small Target Detection

arXiv:2606.01689v1 Announce Type: cross Abstract: The detection and segmentation of infrared small targets have important application significance in the fields of surveillance and security, maritime

T1: Tool-integrated Verification for Test-time Compute Scaling in Small Language Models

arXiv:2504.04718v2 Announce Type: replace-cross Abstract: Recent studies have demonstrated that test-time compute scaling effectively improves the performance of small language models (sLMs). However,

Where to Look: Can Foundation Models Reach a Target Viewpoint Through Active Exploration?

arXiv:2606.01247v1 Announce Type: new Abstract: Humans can reproduce the viewpoint specified by a target image through active head and body motion, yet spatial intelligence in foundation models has la

Diversity Matters: Revisiting Test-Time Compute in Vision-Language Models

arXiv:2605.30713v1 Announce Type: cross Abstract: Test-time compute (TTC) strategies have emerged as a lightweight approach to boost reasoning in large language models (LLMs). However, their applicati

researcharxiv-cs-cv

MADS: Model-Aware Diverse Core Set Selection for Instruction Tuning

arXiv:2605.30857v1 Announce Type: new Abstract: Instruction fine-tuning is employed to enhance the instruction-following ability of large language models (LLMs). As the amount of instruction fine-tuni

Skill Availability and Presentation Granularity in Large-Language-Model Agents: A Controlled SkillsBench Study

arXiv:2605.31408v1 Announce Type: cross Abstract: Skill documents provide procedural knowledge to large-language-model agents at inference time. This article studies whether the presentation granulari

Applications

UniScale: Adaptive Unified Inference Scaling via Online Joint Optimization of Model Routing and Test-Time Scaling

arXiv:2605.30898v1 Announce Type: new Abstract: In real-world deployments of large language models (LLMs), balancing inference quality and computational cost has become a central challenge. Existing a

applicationsarxiv-cs-ai

Your Multimodal Speech Model Says I Have a Face for Radio

arXiv:2605.30472v1 Announce Type: new Abstract: As large neural models have become better at language tasks, researchers are increasingly building multi- and omnimodal models that handle more modaliti

AtomWorld: A Benchmark for Evaluating Spatial Reasoning in Large Language Models on Crystalline Materials

arXiv:2510.04704v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promising potential in scientific research, enabling tasks ranging from knowledge retrieval to propert

Benchmarking Open-Source Safety Guard Models: A Comprehensive Evaluation

arXiv:2605.28830v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly deployed in safety-critical applications, robust content moderation becomes essential. We present a c

Beyond Accuracy: Are Time Series Foundation Models Well-Calibrated?

arXiv:2510.16060v2 Announce Type: replace-cross Abstract: The recent development of foundation models for time series data has generated considerable interest in using such models across a variety of

CosmicFish-HRM: Adaptive Reasoning via Hierarchical Recurrent Mechanisms in Compact Language Models

arXiv:2605.28919v1 Announce Type: cross Abstract: Large language models have achieved strong reasoning capabilities, though often at the cost of massive parameter counts and expensive inference. In th

DenseSteer: Steering Small Language Models towards Dense Math Reasoning

arXiv:2605.29247v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong chain-of-thought (CoT) reasoning abilities, while smaller models (<= 3B parameters) significantly underp

Label-Free Reinforcement Learning via Cross-Model Entropy

arXiv:2605.29009v1 Announce Type: cross Abstract: Post-training large language models with reinforcement learning is bottlenecked by the reward signal. Existing approaches require either ground-truth

Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification

arXiv:2605.29556v1 Announce Type: new Abstract: Building mathematical optimization models is critical in operations research (OR), while it requires substantial human expertise. Recent advancements ha

Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought

arXiv:2603.05488v4 Announce Type: replace-cross Abstract: We provide evidence of performative chain-of-thought (CoT) in reasoning models, where a model becomes strongly confident in its final answer,

Why Far Looks Up: Probing Spatial Representation in Vision-Language Models

arXiv:2605.30161v1 Announce Type: new Abstract: Vision-language models (VLMs) achieve strong performance on spatial reasoning benchmarks, yet it remains unclear whether this reflects structured 3D und

A Simple State Space Model Excels at Multivariate Time Series Classification

arXiv:2605.27406v1 Announce Type: new Abstract: Structured state space models (SSMs) have recently emerged as a promising foundation for sequence modeling, with Mamba-based architectures demonstrating

Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data

arXiv:2605.27618v1 Announce Type: new Abstract: Despite the wide use of explainability techniques to attempt to understand the behavior of Artificial Intelligence (AI), the generated explanations may

Tutorials

Evaluation of AI Ethics Tools in Language Models: A Developers' Perspective Case Study

arXiv:2512.15791v2 Announce Type: replace-cross Abstract: In Artificial Intelligence (AI), language models have gained significant importance due to the widespread adoption of systems capable of simul

tutorialsarxiv-cs-ai

FedMPT: Federated Multi-label Prompt Tuning of Vision-Language Models

arXiv:2605.28347v1 Announce Type: new Abstract: Multi-Label Recognition (MLR) based on Vision-Language Models (VLMs) aims to leverage their pre-trained knowledge to better adapt complex recognition sc

Tutorials

Learning Correlated Reward Models: Statistical Barriers and Opportunities

arXiv:2510.15839v2 Announce Type: replace Abstract: Random Utility Models (RUMs) are a classical framework for modeling user preferences and play a key role in reward modeling for Reinforcement Learni

tutorialsarxiv-cs-lg

PetroBench: A Benchmark for Large Language Models in Petroleum Engineering

arXiv:2605.28032v1 Announce Type: new Abstract: Large Language Models are increasingly applied in the petroleum industry, highlighting the need for a domain-specific evaluation framework. This study d

SONIC-O1: A Real-World Benchmark for Evaluating Multimodal Large Language Models on Audio-Video Understanding

arXiv:2601.21666v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) are a major focus of recent AI research. However, most prior work focuses on static image understanding, wh

When Context Flips, Safety Breaks: Diagnosing Brittle Safety in Aligned Language Models

arXiv:2605.27851v1 Announce Type: new Abstract: Safety benchmark scores provide incomplete evidence of deployment readiness: aligned language models often adhere to rigid rules even when a situational

When Interpretability Is Unequally Distributed: Fairness in Hybrid Interpretable Models

arXiv:2605.28626v1 Announce Type: new Abstract: Hybrid interpretable models combine a transparent component with a black-box model by assigning some examples to the former and deferring the rest to th

Guiding LLM Post-training Data Engineering with Model Internals from Sparse Autoencoders

arXiv:2605.27354v1 Announce Type: cross Abstract: Model internals encode rich information about how a large language model (LLM) processes its training data; however, post-training data engineering la

Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation

arXiv:2511.14993v3 Announce Type: replace-cross Abstract: This report introduces Kandinsky 5.0, a family of state-of-the-art foundation models for high-resolution image and 10-second video synthesis.

Learning to Predict Future-Aligned Research Proposals with Language Models

arXiv:2603.27146v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to assist ideation in research, but evaluating the quality of LLM-generated research proposals re

Pusa V1.0: Unlocking Temporal Control in Pretrained Video Diffusion Models via Vectorized Timestep Adaptation

arXiv:2507.16116v2 Announce Type: replace Abstract: The rapid advancement of video diffusion models has been hindered by fundamental limitations in temporal modeling, particularly the rigid synchroniz

researcharxiv-cs-cv

Recon: Reconstruction-Guided Reasoning Synthesis for User Modeling

arXiv:2605.26969v1 Announce Type: cross Abstract: User modeling aims to use language models (LMs) to mimic an individual's behavior from a corpus of past context-action pairs (e.g., conversation turns

Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline

arXiv:2605.26132v1 Announce Type: new Abstract: Can post-trained large language models (LLMs) further improve themselves using only unlabeled prompts, without external teachers or feedback from tools?

SpatialBench: Is Your Spatial Foundation Model an All-Round Player?

arXiv:2605.27367v1 Announce Type: new Abstract: While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round pla

Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization

arXiv:2603.16105v3 Announce Type: replace-cross Abstract: Post-training model compression is essential for enhancing the portability of Large Language Models (LLMs) while preserving their performance.

26 May 2026

How Well Do Models Follow Their Constitutions?

arXiv:2605.24229v1 Announce Type: new Abstract: Frontier AI developers now train models against long written behavioral specifications, such as Anthropic's constitution (Anthropic, 2025a) and OpenAI's

26 May 2026

Complete-muE: Optimal Hyperparameter Transfer and Scaling for MoE Models

arXiv:2605.23893v1 Announce Type: new Abstract: We propose Complete-muE, a framework which targets hyperparameter transfer across dense FFN and any Mixture-of-Experts (MoE) setups in transformer block

25 May 2026

Lens: Rethinking Training Efficiency for Foundational Text-to-Image Models

arXiv:2605.21573v1 Announce Type: new Abstract: We introduce Lens, a 3.8B-parameter T2I model that achieves performance competitive with, and in several cases surpassing, state-of-the-art models with

22 May 2026

Safety

Reinforcing VLAs in Task-Agnostic World Models

arXiv:2605.12334v2 Announce Type: replace Abstract: Post-training Vision-Language-Action (VLA) models via reinforcement learning (RL) in learned world models has emerged as an effective strategy to ad

safetyarxiv-cs-ai

22 May 2026

Toxic Subword Pruning for Dialogue Response Generation on Large Language Models

arXiv:2410.04155v2 Announce Type: replace Abstract: How to defend large language models (LLMs) from generating toxic content is an important research area. Yet, most research focused on various model

21 May 2026

Beyond Prediction Accuracy: Target-Space Recovery Profiles for Evaluating Model-Brain Alignment

arXiv:2605.20127v1 Announce Type: cross Abstract: Artificial vision models are often evaluated against the human visual cortex by measuring how accurately their internal representations predict brain

Applications

Conflict-Free Replicated Data Types for Neural Network Model Merging: A Two-Layer Architecture Enabling CRDT-Compliant Model Merging Across 26 Strategies

arXiv:2605.19373v1 Announce Type: cross Abstract: All 26 neural network merge strategies we tested including weight averaging, SLERP, TIES, DARE, Fisher merging, and evolutionary approaches -- fail th

applicationsarxiv-cs-ai

Dynamic Model Merging Made Slim

arXiv:2605.18904v1 Announce Type: cross Abstract: Model merging enables the reuse of fine-tuned models without joint training or access to original data. Dynamic merging further improves flexibility b

Eliminating Inductive Bias in Reward Models with Information-Theoretic Guidance

arXiv:2512.23461v2 Announce Type: replace-cross Abstract: Reward models (RMs) are essential in reinforcement learning from human feedback (RLHF) to align large language models (LLMs) with human values

Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models

arXiv:2605.19150v1 Announce Type: cross Abstract: State-space models (SSMs) face a fundamental trade-off between efficiency and expressivity that is mainly dictated by the structure of the model's tra

GVGAI-LLM: Evaluating Large Language Model Agents with Infinite Games

arXiv:2508.08501v3 Announce Type: replace Abstract: We introduce GVGAI-LLM, a video game benchmark for evaluating the reasoning and problem-solving capabilities of large language models (LLMs). Built

19 May 2026

LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation

arXiv:2410.13846v3 Announce Type: replace-cross Abstract: Scaling language models to handle longer contexts introduces substantial memory challenges due to the growing cost of key-value (KV) caches. M